Beyond Structural Insight: A Deep Neural Network for the Prediction of L-2/3-edge X-ray Absorption Spectra
X-ray absorption spectroscopy at the L2/3-edge can be used to obtain detailed information about the local electronic and geometric structure of transition metal complexes. By virtue of dipole selection rules, the transition metal L2/3-edge usually exhibits two distinct spectral regions: i) The white line, which is dominated by bound electronic transitions from metal-centred 2p orbitals into unoccupied orbitals with d character. The intensity and shape of this band consequently reflects the d density of states (d-DOS), which is strongly modulated by mixing with ligand orbitals involved in chemical bonding. ii) The post-edge, where oscillations encode the local geometric structure around the X-ray absorbtion site. In this Article, we extend our recently-developed XANESNET deep neural network (DNN) beyond the K edge to predict X-ray absorption spectra at the Pt L2/3-edge. We demonstrate that XANESNET is able to predict accurately Pt L2/3-edge X-ray absorption spectra, including both the parts containing electronic and geometric structural information. The performance of our DNN in a practical situation is demonstrated by application to three Pt complexes and by prediction of the transient spectrum of a dimeric Pt complex. Our discussion also includes an analysis of the feature importance in our DNN which demonstrates the role of key features and assists with interpreting the performance of the network.